ABSTRACT
BACKGROUND: Research into neural mechanisms underlying cue-induced cigarette craving has attracted considerable attention for its significant role in treatments. However, there is little understanding about the effects of exposure to smoking-related cues on electroencephalogram (EEG) microstates of smokers, which can reflect abnormal brain network activity in several psychiatric disorders. AIMS: To explore whether abnormal brain network activity in smokers on exposure to smoking-related cues would be captured by EEG microstates. METHOD: Forty smokers were exposed to smoking and neutral imagery conditions (cues) during EEG recording. Behavioural data and parameters for microstate topographies associated with the auditory (A), visual (B), salience and memory (C) and dorsal attention networks (D) were compared between conditions. Correlations between microstate parameters and cigarette craving as well as nicotine addiction characteristics were also analysed. RESULTS: The smoking condition elicited a significant increase in the duration of microstate classes B and C and in the duration and contribution of class D compared with the neutral condition. A significant positive correlation between the increased duration of class C (smoking minus neutral) and increased craving ratings was observed, which was fully mediated by increased posterior alpha power. The increased duration and contribution of class D were both positively correlated with years of smoking. CONCLUSIONS: Our results indicate that smokers showed abnormal EEG microstates when exposed to smoking-related cues compared with neutral cues. Importantly, microstate class C (duration) might be a biomarker of cue-induced cigarette craving, and class D (duration and contribution) might reflect the relationship between cue-elicited activation of the dorsal attention network and years of smoking.
ABSTRACT
Compared with the traditional neurofeedback paradigm, the cognition-guided neurofeedback brain-computer interface (BCI) is a novel paradigm with significant effect on nicotine addiction. However, the cognition-guided neurofeedback BCI dataset is extremely lacking at present. This paper provides a BCI dataset based on a novel cognition-guided neurofeedback on nicotine addiction. Twenty-eight participants are recruited and involved in two visits of neurofeedback training. This cognition-guided neurofeedback includes two phases: an offline classifier construction and a real-time neurofeedback training. The original electroencephalogram (EEG) raw data of two phases are provided and evaluated in this paper. The event-related potential (ERP) amplitude and channel waveform suggest that our BCI dataset is of good quality and consistency. During neurofeedback training, the participants' smoking cue reactivity patterns have a significant reduction. The mean accuracy of the multivariate pattern analysis (MVPA) classifier can reach approximately 70%. This novel cognition-guided neurofeedback BCI dataset can be used to develop comparisons with other neurofeedback systems and provide a reference for the development of other BCI algorithms and neurofeedback paradigms on addiction.
